{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's this TensorFlow business?\n",
    "\n",
    "You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.\n",
    "\n",
    "For the last part of this assignment, though, we're going to leave behind your beautiful codebase and instead migrate to one of two popular deep learning frameworks: in this instance, TensorFlow (or PyTorch, if you switch over to that notebook)\n",
    "\n",
    "#### What is it?\n",
    "TensorFlow is a system for executing computational graphs over Tensor objects, with native support for performing backpropogation for its Variables. In it, we work with Tensors which are n-dimensional arrays analogous to the numpy ndarray.\n",
    "\n",
    "#### Why?\n",
    "\n",
    "* Our code will now run on GPUs! Much faster training. Writing your own modules to run on GPUs is beyond the scope of this class, unfortunately.\n",
    "* We want you to be ready to use one of these frameworks for your project so you can experiment more efficiently than if you were writing every feature you want to use by hand. \n",
    "* We want you to stand on the shoulders of giants! TensorFlow and PyTorch are both excellent frameworks that will make your lives a lot easier, and now that you understand their guts, you are free to use them :) \n",
    "* We want you to be exposed to the sort of deep learning code you might run into in academia or industry. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How will I learn TensorFlow?\n",
    "\n",
    "TensorFlow has many excellent tutorials available, including those from [Google themselves](https://www.tensorflow.org/get_started/get_started).\n",
    "\n",
    "Otherwise, this notebook will walk you through much of what you need to do to train models in TensorFlow. See the end of the notebook for some links to helpful tutorials if you want to learn more or need further clarification on topics that aren't fully explained here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import math\n",
    "import timeit\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "    # Subsample the data\n",
    "    mask = range(num_training, num_training + num_validation)\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = range(num_training)\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = range(num_test)\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Model\n",
    "\n",
    "### Some useful utilities\n",
    "\n",
    ". Remember that our image data is initially N x H x W x C, where:\n",
    "* N is the number of datapoints\n",
    "* H is the height of each image in pixels\n",
    "* W is the height of each image in pixels\n",
    "* C is the number of channels (usually 3: R, G, B)\n",
    "\n",
    "This is the right way to represent the data when we are doing something like a 2D convolution, which needs spatial understanding of where the pixels are relative to each other. When we input image data into fully connected affine layers, however, we want each data example to be represented by a single vector -- it's no longer useful to segregate the different channels, rows, and columns of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The example model itself\n",
    "\n",
    "The first step to training your own model is defining its architecture.\n",
    "\n",
    "Here's an example of a convolutional neural network defined in TensorFlow -- try to understand what each line is doing, remembering that each layer is composed upon the previous layer. We haven't trained anything yet - that'll come next - for now, we want you to understand how everything gets set up. \n",
    "\n",
    "In that example, you see 2D convolutional layers (Conv2d), ReLU activations, and fully-connected layers (Linear). You also see the Hinge loss function, and the Adam optimizer being used. \n",
    "\n",
    "Make sure you understand why the parameters of the Linear layer are 5408 and 10.\n",
    "\n",
    "### TensorFlow Details\n",
    "In TensorFlow, much like in our previous notebooks, we'll first specifically initialize our variables, and then our network model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# clear old variables\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# setup input (e.g. the data that changes every batch)\n",
    "# The first dim is None, and gets sets automatically based on batch size fed in\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "def simple_model(X,y):\n",
    "    # define our weights (e.g. init_two_layer_convnet)\n",
    "    \n",
    "    # setup variables\n",
    "    Wconv1 = tf.get_variable(\"Wconv1\", shape=[7, 7, 3, 32])\n",
    "    bconv1 = tf.get_variable(\"bconv1\", shape=[32])\n",
    "    W1 = tf.get_variable(\"W1\", shape=[5408, 10])\n",
    "    b1 = tf.get_variable(\"b1\", shape=[10])\n",
    "\n",
    "    # define our graph (e.g. two_layer_convnet)\n",
    "    a1 = tf.nn.conv2d(X, Wconv1, strides=[1,2,2,1], padding='VALID') + bconv1\n",
    "    h1 = tf.nn.relu(a1)\n",
    "    h1_flat = tf.reshape(h1,[-1,5408])\n",
    "    y_out = tf.matmul(h1_flat,W1) + b1\n",
    "    return y_out\n",
    "\n",
    "y_out = simple_model(X,y)\n",
    "\n",
    "# define our loss\n",
    "total_loss = tf.losses.hinge_loss(tf.one_hot(y,10),logits=y_out)\n",
    "mean_loss = tf.reduce_mean(total_loss)\n",
    "\n",
    "# define our optimizer\n",
    "optimizer = tf.train.AdamOptimizer(5e-4) # select optimizer and set learning rate\n",
    "train_step = optimizer.minimize(mean_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow supports many other layer types, loss functions, and optimizers - you will experiment with these next. Here's the official API documentation for these (if any of the parameters used above were unclear, this resource will also be helpful). \n",
    "\n",
    "* Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn\n",
    "* Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers\n",
    "* BatchNorm: https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model on one epoch\n",
    "While we have defined a graph of operations above, in order to execute TensorFlow Graphs, by feeding them input data and computing the results, we first need to create a `tf.Session` object. A session encapsulates the control and state of the TensorFlow runtime. For more information, see the TensorFlow [Getting started](https://www.tensorflow.org/get_started/get_started) guide.\n",
    "\n",
    "Optionally we can also specify a device context such as `/cpu:0` or `/gpu:0`. For documentation on this behavior see [this TensorFlow guide](https://www.tensorflow.org/tutorials/using_gpu)\n",
    "\n",
    "You should see a validation loss of around 0.4 to 0.6 and an accuracy of 0.30 to 0.35 below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 8.8 and accuracy of 0.062\n",
      "Iteration 100: with minibatch training loss = 1.02 and accuracy of 0.25\n",
      "Iteration 200: with minibatch training loss = 0.746 and accuracy of 0.41\n",
      "Iteration 300: with minibatch training loss = 0.556 and accuracy of 0.41\n",
      "Iteration 400: with minibatch training loss = 0.659 and accuracy of 0.28\n",
      "Iteration 500: with minibatch training loss = 0.507 and accuracy of 0.41\n",
      "Iteration 600: with minibatch training loss = 0.534 and accuracy of 0.36\n",
      "Iteration 700: with minibatch training loss = 0.457 and accuracy of 0.36\n",
      "Epoch 1, Overall loss = 0.748 and accuracy of 0.313\n"
     ]
    },
    {
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ffRPp+ct3OevPH/LUh0vYubuQXbsrr4mszd5VbhbgmSu3RWpMc9fkUFzsZO8q\nKHUnWfTcXZ8s2sS/P1sW92cXkZpR0qhDJx3anuMPbleq7NRe7QEYOahbueN/cuZhLH3oPE4oc85f\nrzyal35wPA9e1I+Rx3ajffM9TVk92u25zffio7vEHeP2Cm7zPf+vH5d6/YvXvmV71JRaa7LzOP+v\nH0dm/gX43cQF9L5nIkfcM4G5YW1mzbZdkSawJRtzOeGhKTz7SekmtQsf/4QHxs9l+rItnPvoR/zq\nzdkc9ev3OOMPH5BXUMTU+RsY8vss3gpvCLj66S+45805cX/Ompq/LodFZfqKRBoSNU/Vs65tmrLs\n4fOqPObJawbw+dLNrNm2i1ZN0ujYMli//KrjDgSgS5smkWPvPq83hcXFHHNgGzbv2M1/vy69LEmL\nxqmM/d8TeOubNTyRtZhz+h4QGTC4tzQyiK5AzF2bQ0oj46w/fwjAkgfPjUyZ8uA781i4PpcHL+5X\nqsYwI2xue/GLFQCsy8njiHsmUDJMZdGG3FIzBefkFfBk1mLaNktn+FGdK4yruNhp1KjylRXzCor4\n9Vtz+J9TD6FH+2Y8/dESBnRvw9EHtokcc/afgwW5Sv6b5RUU8fWKbZxwSLsKrymyv1HS2Ae0aprG\nWX3Kr+lRokvrPbWL0w7rEOlL6Ngygw9+Pphih0cnL+T1r1fz0g+O54hOLTmiU0uuPr47bZumM/qi\nIs79y0esy9nTxDXqhO7cekYvhvw+i+xd5ad3B5h135n0u+89UhtZqYkaO7bIKHWtd2at5Wf/2dPk\n9cniTUxbGiwfX+zwyvSVvDJ9T0c9UOHo+ejZ5B+dvJBHJy+MvF65ZSdPZAUj8n/z9jz+fkZTXvpi\nBf/8ZClv33IKk+au595xs3nymgFMmL2OgT3a0qxxCo1TUziqWysap6Ywcc46Xp62kpenlY5l5j3D\neHf2OpZt2hEpW5+TR8cWjbnnzdmMnb6KD34+mO7tmgHBHWn9urSibbN0Fq7fzsvTVnL3eUdUmrDy\nC4t4bcZqrji2GylVJLWq7MgvJCMtpcbni8RKSWM/0K1tUNO4/qQe5TqfS77IHrq4H6f16kDfLi0j\n+7q0Ds5rkp7C53cNpbComH99uoxz+nWK7Lt1aE/uHz+33HveOSiDFhlpjDy2G2O+LP0l+8crjuKq\nf3wReT1lfulO+Guf2fvra533aOkmtMkrCxi7IKjNPDB+Lt+u2sam3N1cGo5hebrMnWaf3Xk6b32z\ntsJr978CAw7ZAAAU1UlEQVR/Ehlpjcgr2DML8XEPTuaOcw5nyvxgMsqsBRs58ZBi2jZLZ9Sz0+jQ\nojH//eGJjHp2Gmuy8xh1Yne6tgmS+91vzKZg22469Mpm9ursyE0I367axsOXHFnqvTdsz+O7dbmc\n3LN9ubi27thN66ZpFBY7fe6dyJWDDuShi/uVOubD7zbSp3NL2jWvfpLNNdt20bl1kyqPKSgqZuP2\n/GqPk/2XksZ+oFOrJky67VR6tG9W6TEZaSlcWE0fR2pKI75/ysGlyq4/qUckaTx21dEc26MtmS0z\nIusMH31g63JJ48RD2vPCjcdxzTNflCr/7SX9Il+QZxyRyfvz1pcr31vGLthTO3r+8+XVHn/CQ1MA\nOLffAbwzq3xzXXTCKPFwVG3o3nFBv0rLcHngjdvzOeWRqZH9p/0uixH9O3PzkEN5eVrQ5Pbqd6UT\n3ZgvV1JQ5Dx0cT+e+XgpL3y+nPzCIjbl7uaxq47m8ANaRmYGmLZ0S2ScTovGwXu+PG0FO/IL+f1l\nRzF53np6ZrbgumencfSBrXn9/04qF/+KzTuZNG892bsKaJqewsPvzufJa44hA1i4fjvXPTuNV394\nYuQHxIzlW7n/rTl8syqbKT89jYM7NK/275pM8gqKmLZ0C6f0bF9ujZxEKCwq3i/nhFPS2E/0rGRK\nk9oyM6b+bDBrt+3ixEPL/9q9fGA3Vm3dxV+nLGJA9zb069IKgOMObss1xx/IRUd35Y2vV/P858s5\n7ICWvPT941i9bRcj+neh193vAnBOv07cO24OeQXF/GVkf+at3c61J3TnpIeDL/JfX9CHe8fN4dIB\nXfl/Z/SkcWoKm3Lzad00jVMfmUq3Nk254eSDOK1XB75asZVbx8wEgvXdO7XK4Lv1wfK7lw3oyn9m\nBDMJN0tPYUeZO7t6ZTbnj5f3Z3vedD5auKncZ/2fUw+moMjLdd5Hq2qU/5sz11Q6O0CJ175axdcr\ntrIkqikM4EcvfQ3Agxf1i6xDXyL65oVx36zhgFYZPPXhkkjZ1yu2MXt1Nn3D/zY5eQUc/+BkdlZw\nZ9v/vvAVg7ulsvbrr1mbncfjUxdx0iHteX/eel6P6h+btTqbtdl5PPnBYgqKipmzOoeRg7rxy/N6\nR47JWrCBHflF/P3DxTwz6lhmr8lmcK8ObMzNp2OLjMhxD707j4++28Q7t57Clh27adssuLHj8yWb\nuW/cHIYf1ZnWTdO4atCBpb7sP1u8mdXbdtGtTRP++9VqTurZngvC/qzsnQW0aprGmm27OPHhKfz7\nhkEs3pjLr98KfgB99IshtGuezgPj53JEp5Zcc1z3SPPhgnXbaZqeQreoueO27NjNPW/O5jcX9qV1\n03TembWWkw5pT6umaZFzdu4u5OgD27BgSxHfCwfZvv+T0yKJ/rUZq3jxi+X85sJ+HNGpBfmFxaSn\nNKq02TKvoIhdu4to0yy9wv31xfblZUcHDhzo06dPr/H5WVlZDB48eO8FtBclc2xQOr6iYuftWWs5\nv1+nCv8BuDtz1uREvrRKvDNrLTt3F3HpgK4s2ZjLnDU5pTqxP/huI2kpxjEHtmHK/A2ccUQm6amN\nyl277K/Gy578lIVrt/HFr86icWoKfe6ZwI7dRfx0WC++WrGVz5Zs5rM7hvLbCfMZ8+VK3rvtVDbk\n5HPSoe0wM7bu2M3Jv53CaYd1oLh4z+DKqT8bTI92Tbniqc8jfTJlHdAyg5y8AnbuLuKsPplMnLM+\n7r9tohyW2QIzGHJ4R/6WVfmMzLH439MOYdzM1eVu9b5yUDfuH9GXSXPX838vflXp+U9fN5DfvD2X\nwmJn1dZg5oGnrh3ATc/P4PwjOzH0iI6lbv0GSE9pxPCjOrNs9Vpe/NEwDv/VhHLXnXnPMAb/Pott\nOws4+sDWDDmsI3+c9B0AXds0ibxXWf++YRCn9urA+pw8jntwMukpjfhu9DmR/Q+9M4+/f7iEu849\nnAv7d2HQg5Ppldmc1k3TS/2/sOzh87j60Yl8siZI5H8Z2Z8R/YMafr/7JkbuMLz+pB7885Nl9O3S\nktMPC+age/uWk/lw4SZOPKQdE2av48cvfx255qeLN/Hsx0t5/OpjWLF5Jxu253NSBT/iYpGVlcWQ\nIUNmuPvAmpyvpJGkX8zJHBskd3yFRcV88MEHDD19CBAkloUbcunermlkUseS43bsLqJVk7QKr5HS\nyPjBv2dEmtEWP3hupKP5zD99EKnBjOjfmV+d35sF67ZzRKeWLN20g8Ubcznh4Hac+acP2VVQREZa\nI76590wOuzv4omuZbnxw+xm0bprG858vZ1jvTL5dlU2/Lq04MaxhndarAx9ELeAV7ew+B0SS2UMX\n92Po4R0Z9ODkGv29RvTvTLc2TXls6qKYjj+4fbNyNaFoFdXikt3BHZrx+FXHcM5fPoqU3XnO4RzT\nvQ3vzFrLyi07eX/eBu4f0YejurZmxOOfVHidLq2bRKb6KXFKz/bcNqwXFz/xaUyxXHJM11Jr69x2\nRi/+9H6Q+J67YRA3v/gVufmFTLtraOROynjUNmmoeUr2O6kpjUrdRWRmFc5InJrSiFZNKm5zLmmL\nbhzWbH4yrFepa75zyynkFxbz9EdLuf7kHrTMSKP9oUFnc9tm6QwIB19+9athHHHPBC4d0JXGqSmM\n//HJtGmWzsKZX0SaHa47oQcQ9E1Fe+6GQUyZv54b/rXnh9EVA7vx20uDzvLLnvyUL5dtpXvbpnRs\nmcGfr+hP9q4C0lIa8cr0lfzszF6R6Wue/XhphTc0QFBrOKJTy3JJ45FLjuQXr31bquzGkw/imAPb\ncPNLpWsRi0afQ0GRc/2/pvH5ktK1sCsHHRjpx4lH/26t6d25JS99Uf25/bq0Ytbq7GqPm/qzwVz8\nxCds3Vn6jsAlG3eUShhQ8R181Y0JKpswIFjuoKLmzsqUXYytJGEAjHp2z00k367K5oze8SeN2lLS\nEKnCvcN7k9kygx8OPqRUeWpKI1JTGnHrGT2rPL9JegrTfjmUtuE0KyVNdAurOilKz457kt3Unw3m\noKibHUZf1I/Rb8+LjCOJvtGhZAxPietP6sFR3VrTIiOVb1dls3LLTv4yeSEnHtKOww8I3uO9204l\nN7+Q2TO/4vRTTqBrm6b07dKKA1plsHLLTrq2aRK5C+uQjqdw9p8/Ii3FePCifuHfA/75vUEM+9MH\nkWagktrZXeceTr/73qNFRmqpQaA/Pv1Q2jYL+hZ6d25JUTHMW5vDP68/liGHdaSo2Pnx6Ycyd00O\nf3r/O2avzmH4IWnccsEJHNS+GSu37mLI77M48dB2XHJMF3bsLqJts3S+WbmN3PxCWjZJ48enH8oJ\nD03hsMwWHNS+GXef15uH3p3HOX07ccIh7apsRovFtcd35/4RfXhk4gK+XrGVY1ru4JqzT+K2V2by\nRZlmzIcv7seFR3fhvnFzGHRQW347YT7rc/JLHdM0PYUR/btw3wW9eWfWWtZm5zFm2ko2bM8jr6CY\nkw5txyeLNrNlR2zTDO117r7PPgYMGOC1MXXq1Fqdn0jJHJu74qutquL7YMEGnzB7beT1hpw8zy8o\n2qvvX1BY5K9OX+kFheWvG+vfbtfuQi8sKq5w37y12b5wfU6psiemLvK5a7J97prsUp/P3X1nfqHn\nFRT6K9NWePfbx3vOrt3lrrl22y7/4QvTffx7U0qVz1mdXeHniDZzxVbfnJtf4b6fjZ3p9781x79Z\nudWnL9vs+QVFXlRU7AvXb/fut4/3s/70gS9cn+Pdbx/v3W8f7/e/NSeyfe0zX3heQWGp60X//aYv\n2+IfL9wYOX7SnHWljn3pi+Xe/fbxfv0/p3n328f7lU995sXFFf9N1+fs8u/W5XhuXoF3v328/y1r\nUZWfuTJTp051YLrX8HtXNQ2RJHNqmfVOOrSofoxFvFJTGnHJgK61ukZGWkql+w4/oGW5suja2hGd\nSu8vWffl8mO7cfmx5afUATigVQZPXD0gcrt3id6dy79XWUeVmdct2u8uO6rC8kM7NmfR6HModkhP\nbcQfLz+KA1plcOIh7endqSU//c83XDWoW6l+srJKmim//tUw/v3ZcgYfVvq/7ZWDDuTC/l1okp7C\nxu35tG2WXuntwB1bZNCxRQbuTuPURvVW01DSEBGpRPQ4i4uP6Rq13YWDOzQrNcVMVdo0S6+0KbMk\nYcb648DMaNcsnc25ShoiIvsEM4s5YSTCmX0OKDWOpC4paYiI7GPuu6BPvb33/jfGXUREEibpkoaZ\nnW1mC8xskZndUd/xiIjIHkmVNMwsBXgcOAfoDVxpZr2rPktEROpKUiUNYBCwyN2XuPtuYAwwop5j\nEhGRUFLNPWVmlwJnu/v3w9fXAse5+4+ijrkJuAkgMzNzwJgxY2r8frm5uTRvnpzTOydzbKD4aiuZ\n40vm2EDx1VZubi7Dhw9vOHNPuftTwFMQTFhYm0nzknnSvWSODRRfbSVzfMkcGyi+2io7ODJeydY8\ntRqIHg7aNSwTEZEkkGxJ40ugp5kdZGbpwEhgXD3HJCIioaTq0wAws3OBPwMpwLPuPrqKYzcC1a/l\nWbn2QOxzFtetZI4NFF9tJXN8yRwbKL7aag80c/cO1R5ZgaRLGnXJzKbXtDMo0ZI5NlB8tZXM8SVz\nbKD4aqu28SVb85SIiCQxJQ0REYlZQ08aT9V3AFVI5thA8dVWMseXzLGB4qutWsXXoPs0REQkPg29\npiEiInFQ0hARkZg1yKSRDNOvm9mzZrbBzGZHlbU1s0lmtjB8bhO1784w3gVmdlaCY+tmZlPNbK6Z\nzTGzW5Msvgwzm2Zm34Tx/TqZ4ot6zxQz+9rMxidbfGa2zMxmmdlMM5ueTPGZWWsze9XM5pvZPDM7\nIYliOyz8m5U8cszs/yVLfOH73Rb+u5htZi+H/172Xnzu3qAeBIMGFwMHA+nAN0DveojjVOAYYHZU\n2SPAHeH2HcBvw+3eYZyNgYPC+FMSGFsn4JhwuwXwXRhDssRnQPNwOw34Ajg+WeKLivMnwEvA+GT6\n7xu+5zKgfZmypIgPeA74fridDrROltjKxJkCrAO6J0t8QBdgKdAkfD0W+N7ejC/hf9hkewAnABOj\nXt8J3FlPsfSgdNJYAHQKtzsBCyqKEZgInFCHcb4JDEvG+ICmwFfAcckUH8G8aZOB09mTNJIpvmWU\nTxr1Hh/QKvzSs2SLrYJYzwQ+Sab4CJLGSqAtwYS048M491p8DbF5quSPWmJVWJYMMt19bbi9DsgM\nt+stZjPrARxN8Gs+aeILm35mAhuASe6eVPERTIXzC6A4qiyZ4nPgfTObYcFyA8kS30HARuCfYdPe\n02bWLEliK2sk8HK4nRTxuftq4PfACmAtkO3u7+3N+Bpi0tgneJD26/V+aDNrDrwG/D93z4neV9/x\nuXuRu/cn+EU/yMz6ltlfb/GZ2fnABnefUdkx9f33A04O/37nADeb2anRO+sxvlSCZtu/ufvRwA6C\n5pRkiC3CgglVLwD+U3ZfPf+/14Zg4bqDgM5AMzO7JvqY2sbXEJNGMk+/vt7MOgGEzxvC8jqP2czS\nCBLGi+7+32SLr4S7bwOmAmcnUXwnAReY2TKC1SdPN7MXkii+kl+kuPsG4HWCVTOTIb5VwKqw5gjw\nKkESSYbYop0DfOXu68PXyRLfGcBSd9/o7gXAf4ET92Z8DTFpJPP06+OAUeH2KIK+hJLykWbW2MwO\nAnoC0xIVhJkZ8Awwz93/mITxdTCz1uF2E4L+lvnJEp+73+nuXd29B8H/X1Pc/Zpkic/MmplZi5Jt\ngjbv2ckQn7uvA1aa2WFh0VBgbjLEVsaV7GmaKokjGeJbARxvZk3Df8dDgXl7Nb666DBKtgdwLsEd\nQYuBX9ZTDC8TtDkWEPy6uhFoR9B5uhB4H2gbdfwvw3gXAOckOLaTCaqv3wIzw8e5SRTfkcDXYXyz\ngXvC8qSIr0ysg9nTEZ4U8RHcOfhN+JhT8m8gieLrD0wP//u+AbRJltjC92sGbAZaRZUlU3y/JvgR\nNRt4nuDOqL0Wn6YRERGRmDXE5ikREakhJQ0REYmZkoaIiMRMSUNERGKmpCEiIjFT0pD9hpldYNXM\nWmxmnc3s1XD7e2b2WJzvcVcMx/zLzC6N57p7k5llmdnA+np/2b8pach+w93HufvD1Ryzxt1r84Ve\nbdLYl5lZan3HIMlNSUOSnpn1CNdW+JeZfWdmL5rZGWb2Sbg+wKDwuEjNITz2UTP71MyWlPzyD681\nO+ry3cJf5gvN7N6o93wjnMxvTsmEfmb2MNDEgnUUXgzLrjOzby1Y2+P5qOueWva9K/hM88zsH+F7\nvBeObi9VUzCz9uF0JCWf7w0L1kNYZmY/MrOfhBP7fW5mbaPe4towztlRf59mFqzjMi08Z0TUdceZ\n2RSCAWAilVLSkH3FocAfgMPDx1UEI9d/RuW//juFx5wPVFYDGQRcQjDK/LKoZp0b3H0AMBC4xcza\nufsdwC537+/uV5tZH+Bu4HR3Pwq4Nc737gk87u59gG1hHNXpC1wMHAuMBnZ6MLHfZ8B1Ucc19WBC\nwv8Dng3LfkkwpckgYAjwu3AaEQjmd7rU3U+LIQZpwJQ0ZF+x1N1nuXsxwdQXkz2YzmAWwbokFXnD\n3YvdfS57poIua5K7b3b3XQSTu50clt9iZt8AnxNM6NazgnNPB/7j7psA3H1LnO+91N1nhtszqvgc\n0aa6+3Z33whkA2+F5WX/Di+HMX0ItAzn6joTuMOCKeWzgAzgwPD4SWXiF6mQ2i9lX5EftV0c9bqY\nyv8/jj7HKjmm7Dw6bmaDCWYLPcHdd5pZFsEXbDxiee/oY4qAJuF2IXt+0JV931j/DuU+VxjHJe6+\nIHqHmR1HMAW5SLVU05CGbpgF6yc3AS4EPiFYPW5rmDAOJ1hKtkSBBdPGA0whaNJqB8Ea23sppmXA\ngHC7pp32VwCY2ckEC/FkE6zK9uNw9lPM7OhaxikNkJKGNHTTCNYN+RZ4zd2nAxOAVDObR9Af8XnU\n8U8B35rZi+4+h6Bf4YOwKeuP7B2/B35oZl8D7Wt4jbzw/CcJZlAGeIBgTfVvzWxO+FokLprlVkRE\nYqaahoiIxExJQ0REYqakISIiMVPSEBGRmClpiIhIzJQ0REQkZkoaIiISs/8PZBxiw0Rv7vQAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148dce83ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.45 and accuracy of 0.379\n"
     ]
    }
   ],
   "source": [
    "def run_model(session, predict, loss_val, Xd, yd,\n",
    "              epochs=1, batch_size=64, print_every=100,\n",
    "              training=None, plot_losses=False):\n",
    "    # have tensorflow compute accuracy\n",
    "    correct_prediction = tf.equal(tf.argmax(predict,1), y)\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    \n",
    "    # shuffle indicies\n",
    "    train_indicies = np.arange(Xd.shape[0])\n",
    "    np.random.shuffle(train_indicies)\n",
    "\n",
    "    training_now = training is not None\n",
    "    \n",
    "    # setting up variables we want to compute (and optimizing)\n",
    "    # if we have a training function, add that to things we compute\n",
    "    variables = [mean_loss,correct_prediction,accuracy]\n",
    "    if training_now:\n",
    "        variables[-1] = training\n",
    "    \n",
    "    # counter \n",
    "    iter_cnt = 0\n",
    "    for e in range(epochs):\n",
    "        # keep track of losses and accuracy\n",
    "        correct = 0\n",
    "        losses = []\n",
    "        # make sure we iterate over the dataset once\n",
    "        for i in range(int(math.ceil(Xd.shape[0]/batch_size))):\n",
    "            # generate indicies for the batch\n",
    "            start_idx = (i*batch_size)%Xd.shape[0]\n",
    "            idx = train_indicies[start_idx:start_idx+batch_size]\n",
    "            \n",
    "            # create a feed dictionary for this batch\n",
    "            feed_dict = {X: Xd[idx,:],\n",
    "                         y: yd[idx],\n",
    "                         is_training: training_now }\n",
    "            # get batch size\n",
    "            actual_batch_size = yd[idx].shape[0]\n",
    "            \n",
    "            # have tensorflow compute loss and correct predictions\n",
    "            # and (if given) perform a training step\n",
    "            loss, corr, _ = session.run(variables,feed_dict=feed_dict)\n",
    "            \n",
    "            # aggregate performance stats\n",
    "            losses.append(loss*actual_batch_size)\n",
    "            correct += np.sum(corr)\n",
    "            \n",
    "            # print every now and then\n",
    "            if training_now and (iter_cnt % print_every) == 0:\n",
    "                print(\"Iteration {0}: with minibatch training loss = {1:.3g} and accuracy of {2:.2g}\"\\\n",
    "                      .format(iter_cnt,loss,np.sum(corr)/actual_batch_size))\n",
    "            iter_cnt += 1\n",
    "        total_correct = correct/Xd.shape[0]\n",
    "        total_loss = np.sum(losses)/Xd.shape[0]\n",
    "        print(\"Epoch {2}, Overall loss = {0:.3g} and accuracy of {1:.3g}\"\\\n",
    "              .format(total_loss,total_correct,e+1))\n",
    "        if plot_losses:\n",
    "            plt.plot(losses)\n",
    "            plt.grid(True)\n",
    "            plt.title('Epoch {} Loss'.format(e+1))\n",
    "            plt.xlabel('minibatch number')\n",
    "            plt.ylabel('minibatch loss')\n",
    "            plt.show()\n",
    "    return total_loss,total_correct\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    with tf.device(\"/cpu:0\"): #\"/cpu:0\" or \"/gpu:0\" \n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        print('Training')\n",
    "        run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step,True)\n",
    "        print('Validation')\n",
    "        run_model(sess,y_out,mean_loss,X_val,y_val,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a specific model\n",
    "\n",
    "In this section, we're going to specify a model for you to construct. The goal here isn't to get good performance (that'll be next), but instead to get comfortable with understanding the TensorFlow documentation and configuring your own model. \n",
    "\n",
    "Using the code provided above as guidance, and using the following TensorFlow documentation, specify a model with the following architecture:\n",
    "\n",
    "* 7x7 Convolutional Layer with 32 filters and stride of 1\n",
    "* ReLU Activation Layer\n",
    "* Spatial Batch Normalization Layer (trainable parameters, with scale and centering)\n",
    "* 2x2 Max Pooling layer with a stride of 2\n",
    "* Affine layer with 1024 output units\n",
    "* ReLU Activation Layer\n",
    "* Affine layer from 1024 input units to 10 outputs\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# clear old variables\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# define our input (e.g. the data that changes every batch)\n",
    "# The first dim is None, and gets sets automatically based on batch size fed in\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "# define model\n",
    "def complex_model(X,y,is_training):\n",
    "    \n",
    "    # Set up variables\n",
    "    Wconv1 = tf.get_variable(\"Wconv1\",shape=[7,7,3,32])\n",
    "    bconv1 = tf.get_variable(\"bconv1\",shape=[32])\n",
    "    Waffine1 = tf.get_variable(\"Waffine1\", shape=[5408,1024])\n",
    "    baffine1 = tf.get_variable(\"baffine1\", shape=[1024])\n",
    "    Waffine2 = tf.get_variable(\"Waffine2\", shape=[1024,10])\n",
    "    baffine2 = tf.get_variable(\"baffine2\", shape=[10])\n",
    "    \n",
    "    # Define model\n",
    "    conv1act = tf.nn.conv2d(X, Wconv1, strides=[1,1,1,1], padding='VALID') + bconv1\n",
    "    relu1act = tf.nn.relu(conv1act)\n",
    "    batchnorm1act = tf.layers.batch_normalization(relu1act, training=is_training)  # Using the tf.layers batch norm as its easier\n",
    "    pool1act = tf.nn.max_pool(batchnorm1act, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')\n",
    "    flatten1 = tf.reshape(pool1act,[-1,5408])\n",
    "    affine1act = tf.matmul(flatten1, Waffine1) + baffine1\n",
    "    relu2act = tf.nn.relu(affine1act)\n",
    "    y_out = tf.matmul(relu2act, Waffine2) + baffine2\n",
    "    \n",
    "    return y_out\n",
    "\n",
    "y_out = complex_model(X,y,is_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure you're doing the right thing, use the following tool to check the dimensionality of your output (it should be 64 x 10, since our batches have size 64 and the output of the final affine layer should be 10, corresponding to our 10 classes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 86.3 ms per loop\n",
      "(64, 10)\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Now we're going to feed a random batch into the model \n",
    "# and make sure the output is the right size\n",
    "x = np.random.randn(64, 32, 32,3)\n",
    "with tf.Session() as sess:\n",
    "    with tf.device(\"/cpu:0\"): #\"/cpu:0\" or \"/gpu:0\"\n",
    "        tf.global_variables_initializer().run()\n",
    "\n",
    "        ans = sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "        %timeit sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "        print(ans.shape)\n",
    "        print(np.array_equal(ans.shape, np.array([64, 10])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see the following from the run above \n",
    "\n",
    "`(64, 10)`\n",
    "\n",
    "`True`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPU!\n",
    "\n",
    "Now, we're going to try and start the model under the GPU device, the rest of the code stays unchanged and all our variables and operations will be computed using accelerated code paths. However, if there is no GPU, we get a Python exception and have to rebuild our graph. On a dual-core CPU, you might see around 50-80ms/batch running the above, while the Google Cloud GPUs (run below) should be around 2-5ms/batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    with tf.Session() as sess:\n",
    "        with tf.device(\"/gpu:0\") as dev: #\"/cpu:0\" or \"/gpu:0\"\n",
    "            tf.global_variables_initializer().run()\n",
    "\n",
    "            ans = sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "            %timeit sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "except tf.errors.InvalidArgumentError:\n",
    "    print(\"no gpu found, please use Google Cloud if you want GPU acceleration\")    \n",
    "    # rebuild the graph\n",
    "    # trying to start a GPU throws an exception \n",
    "    # and also trashes the original graph\n",
    "    tf.reset_default_graph()\n",
    "    X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "    y = tf.placeholder(tf.int64, [None])\n",
    "    is_training = tf.placeholder(tf.bool)\n",
    "    y_out = complex_model(X,y,is_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should observe that even a simple forward pass like this is significantly faster on the GPU. So for the rest of the assignment (and when you go train your models in assignment 3 and your project!), you should use GPU devices. However, with TensorFlow, the default device is a GPU if one is available, and a CPU otherwise, so we can skip the device specification from now on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model.\n",
    "\n",
    "Now that you've seen how to define a model and do a single forward pass of some data through it, let's  walk through how you'd actually train one whole epoch over your training data (using the complex_model you created provided above).\n",
    "\n",
    "Make sure you understand how each TensorFlow function used below corresponds to what you implemented in your custom neural network implementation.\n",
    "\n",
    "First, set up an **RMSprop optimizer** (using a 1e-3 learning rate) and a **cross-entropy loss** function. See the TensorFlow documentation for more information\n",
    "* Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn\n",
    "* Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Inputs\n",
    "#     y_out: is what your model computes\n",
    "#     y: is your TensorFlow variable with label information\n",
    "# Outputs\n",
    "#    mean_loss: a TensorFlow variable (scalar) with numerical loss\n",
    "#    optimizer: a TensorFlow optimizer\n",
    "# This should be ~3 lines of code!\n",
    "mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=tf.one_hot(y,10), logits=y_out))\n",
    "optimizer = tf.train.RMSPropOptimizer(learning_rate=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# batch normalization in tensorflow requires this extra dependency\n",
    "extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "with tf.control_dependencies(extra_update_ops):\n",
    "    train_step = optimizer.minimize(mean_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model\n",
    "Below we'll create a session and train the model over one epoch. You should see a loss of 1.4 to 2.0 and an accuracy of 0.4 to 0.5. There will be some variation due to random seeds and differences in initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 3.67 and accuracy of 0.062\n",
      "Iteration 100: with minibatch training loss = 2.17 and accuracy of 0.44\n",
      "Iteration 200: with minibatch training loss = 2.38 and accuracy of 0.38\n",
      "Iteration 300: with minibatch training loss = 1.83 and accuracy of 0.44\n",
      "Iteration 400: with minibatch training loss = 1.26 and accuracy of 0.55\n",
      "Iteration 500: with minibatch training loss = 1.29 and accuracy of 0.53\n",
      "Iteration 600: with minibatch training loss = 1.47 and accuracy of 0.48\n",
      "Iteration 700: with minibatch training loss = 1.31 and accuracy of 0.53\n",
      "Epoch 1, Overall loss = 1.68 and accuracy of 0.453\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.6846860371609123, 0.45287755102040816)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "\n",
    "sess.run(tf.global_variables_initializer())\n",
    "print('Training')\n",
    "run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the accuracy of the model.\n",
    "\n",
    "Let's see the train and test code in action -- feel free to use these methods when evaluating the models you develop below. You should see a loss of 1.3 to 2.0 with an accuracy of 0.45 to 0.55."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 1.34 and accuracy of 0.555\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.3391396274566651, 0.55500000000000005)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Validation')\n",
    "run_model(sess,y_out,mean_loss,X_val,y_val,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a _great_ model on CIFAR-10!\n",
    "\n",
    "Now it's your job to experiment with architectures, hyperparameters, loss functions, and optimizers to train a model that achieves ** >= 70% accuracy on the validation set** of CIFAR-10. You can use the `run_model` function from above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Things you should try:\n",
    "- **Filter size**: Above we used 7x7; this makes pretty pictures but smaller filters may be more efficient\n",
    "- **Number of filters**: Above we used 32 filters. Do more or fewer do better?\n",
    "- **Pooling vs Strided Convolution**: Do you use max pooling or just stride convolutions?\n",
    "- **Batch normalization**: Try adding spatial batch normalization after convolution layers and vanilla batch normalization after affine layers. Do your networks train faster?\n",
    "- **Network architecture**: The network above has two layers of trainable parameters. Can you do better with a deep network? Good architectures to try include:\n",
    "    - [conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n",
    "    - [conv-relu-conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n",
    "    - [batchnorm-relu-conv]xN -> [affine]xM -> [softmax or SVM]\n",
    "- **Use TensorFlow Scope**: Use TensorFlow scope and/or [tf.layers](https://www.tensorflow.org/api_docs/python/tf/layers) to make it easier to write deeper networks. See [this tutorial](https://www.tensorflow.org/tutorials/layers) for how to use `tf.layers`. \n",
    "- **Use Learning Rate Decay**: [As the notes point out](http://cs231n.github.io/neural-networks-3/#anneal), decaying the learning rate might help the model converge. Feel free to decay every epoch, when loss doesn't change over an entire epoch, or any other heuristic you find appropriate. See the [Tensorflow documentation](https://www.tensorflow.org/versions/master/api_guides/python/train#Decaying_the_learning_rate) for learning rate decay.\n",
    "- **Global Average Pooling**: Instead of flattening and then having multiple affine layers, perform convolutions until your image gets small (7x7 or so) and then perform an average pooling operation to get to a 1x1 image picture (1, 1 , Filter#), which is then reshaped into a (Filter#) vector. This is used in [Google's Inception Network](https://arxiv.org/abs/1512.00567) (See Table 1 for their architecture).\n",
    "- **Regularization**: Add l2 weight regularization, or perhaps use [Dropout as in the TensorFlow MNIST tutorial](https://www.tensorflow.org/get_started/mnist/pros)\n",
    "\n",
    "### Tips for training\n",
    "For each network architecture that you try, you should tune the learning rate and regularization strength. When doing this there are a couple important things to keep in mind:\n",
    "\n",
    "- If the parameters are working well, you should see improvement within a few hundred iterations\n",
    "- Remember the coarse-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n",
    "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n",
    "- You should use the validation set for hyperparameter search, and we'll save the test set for evaluating your architecture on the best parameters as selected by the validation set.\n",
    "\n",
    "### Going above and beyond\n",
    "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these; however they would be good things to try for extra credit.\n",
    "\n",
    "- Alternative update steps: For the assignment we implemented SGD+momentum, RMSprop, and Adam; you could try alternatives like AdaGrad or AdaDelta.\n",
    "- Alternative activation functions such as leaky ReLU, parametric ReLU, ELU, or MaxOut.\n",
    "- Model ensembles\n",
    "- Data augmentation\n",
    "- New Architectures\n",
    "  - [ResNets](https://arxiv.org/abs/1512.03385) where the input from the previous layer is added to the output.\n",
    "  - [DenseNets](https://arxiv.org/abs/1608.06993) where inputs into previous layers are concatenated together.\n",
    "  - [This blog has an in-depth overview](https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32)\n",
    "\n",
    "If you do decide to implement something extra, clearly describe it in the \"Extra Credit Description\" cell below.\n",
    "\n",
    "### What we expect\n",
    "At the very least, you should be able to train a ConvNet that gets at **>= 70% accuracy on the validation set**. This is just a lower bound - if you are careful it should be possible to get accuracies much higher than that! Extra credit points will be awarded for particularly high-scoring models or unique approaches.\n",
    "\n",
    "You should use the space below to experiment and train your network. The final cell in this notebook should contain the training and validation set accuracies for your final trained network.\n",
    "\n",
    "Have fun and happy training!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Feel free to play with this cell\n",
    "\n",
    "def my_model(X,y,is_training):\n",
    "    \n",
    "    # Conv-Relu-BN\n",
    "    conv1act = tf.layers.conv2d(inputs=X, filters=32, padding='same', kernel_size=3, strides=1, activation=tf.nn.relu)\n",
    "    bn1act = tf.layers.batch_normalization(inputs=conv1act, training=is_training)\n",
    "    # Conv-Relu-BN\n",
    "    conv2act = tf.layers.conv2d(inputs=bn1act, filters=64, padding='same', kernel_size=3, strides=1, activation=tf.nn.relu)\n",
    "    bn2act = tf.layers.batch_normalization(inputs=conv2act, training=is_training)\n",
    "    # Maxpool\n",
    "    maxpool1act = tf.layers.max_pooling2d(inputs=bn2act, pool_size=2, strides=2)\n",
    "    # Flatten\n",
    "    flatten1 = tf.reshape(maxpool1act,[-1,16384])\n",
    "    # FC-Relu-BN\n",
    "    fc1 = tf.layers.dense(inputs=flatten1, units=1024 , activation=tf.nn.relu)\n",
    "    bn3act = tf.layers.batch_normalization(inputs=fc1, training=is_training)\n",
    "    # Output FC \n",
    "    y_out = tf.layers.dense(inputs=bn3act, units=10, activation=None)\n",
    "    \n",
    "    return y_out\n",
    "    \n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "y_out = my_model(X,y,is_training)\n",
    "\n",
    "mean_loss = tf.losses.softmax_cross_entropy(logits=y_out, onehot_labels=tf.one_hot(y,10))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=0.001)\n",
    "\n",
    "# batch normalization in tensorflow requires this extra dependency\n",
    "extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "with tf.control_dependencies(extra_update_ops):\n",
    "    train_step = optimizer.minimize(mean_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 2.8 and accuracy of 0.094\n",
      "Iteration 100: with minibatch training loss = 1.27 and accuracy of 0.53\n",
      "Iteration 200: with minibatch training loss = 1.49 and accuracy of 0.38\n",
      "Iteration 300: with minibatch training loss = 1.32 and accuracy of 0.52\n",
      "Iteration 400: with minibatch training loss = 1.28 and accuracy of 0.55\n",
      "Iteration 500: with minibatch training loss = 1.3 and accuracy of 0.5\n",
      "Iteration 600: with minibatch training loss = 1.1 and accuracy of 0.62\n",
      "Iteration 700: with minibatch training loss = 1.41 and accuracy of 0.47\n",
      "Epoch 1, Overall loss = 1.29 and accuracy of 0.565\n"
     ]
    },
    {
     "data": {
      "image/png": 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gxPPp3aNgjtM9OCMjI/jZvHp070ZGxjGc/eSnrNgmZE3KIPPActiQRcv2nWgJ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\nVUlNSqRX++Y0S00iOaxr4pQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x148cb826278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 800: with minibatch training loss = 0.831 and accuracy of 0.73\n",
      "Iteration 900: with minibatch training loss = 0.8 and accuracy of 0.69\n",
      "Iteration 1000: with minibatch training loss = 0.783 and accuracy of 0.7\n",
      "Iteration 1100: with minibatch training loss = 0.872 and accuracy of 0.64\n",
      "Iteration 1200: with minibatch training loss = 1 and accuracy of 0.61\n",
      "Iteration 1300: with minibatch training loss = 0.742 and accuracy of 0.73\n",
      "Iteration 1400: with minibatch training loss = 0.782 and accuracy of 0.69\n",
      "Iteration 1500: with minibatch training loss = 0.757 and accuracy of 0.73\n",
      "Epoch 2, Overall loss = 0.838 and accuracy of 0.712\n"
     ]
    },
    {
     "data": {
      "image/png": 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jWBD0xyOK8b4dd8LByoJmXh9A0rwaUc5A3ZqD30NgO6S9wmHLgaM4+77XsONQU2S0cRSj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\nX7nERZ+PzyFBUi2abzvQaDjO++UYqRgFCu2Q/g2AbwEQU0e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      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c98f97b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1600: with minibatch training loss = 0.881 and accuracy of 0.73\n",
      "Iteration 1700: with minibatch training loss = 0.726 and accuracy of 0.78\n",
      "Iteration 1800: with minibatch training loss = 0.61 and accuracy of 0.78\n",
      "Iteration 1900: with minibatch training loss = 0.386 and accuracy of 0.89\n",
      "Iteration 2000: with minibatch training loss = 0.596 and accuracy of 0.81\n",
      "Iteration 2100: with minibatch training loss = 0.387 and accuracy of 0.88\n",
      "Iteration 2200: with minibatch training loss = 0.58 and accuracy of 0.77\n",
      "Epoch 3, Overall loss = 0.573 and accuracy of 0.803\n"
     ]
    },
    {
     "data": {
      "image/png": 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wak4kMW/Dvsgc0kAw5yw/v+wShukMkoJZqbaxBf3GTTDkRAper/G7lzauPZDA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\nxk4EcDqA7xLRiQDGAZjKGDsOwFT9u0KRFfhdHc+Jwrj3Vdu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c9985f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 2300: with minibatch training loss = 0.309 and accuracy of 0.92\n",
      "Iteration 2400: with minibatch training loss = 0.623 and accuracy of 0.81\n",
      "Iteration 2500: with minibatch training loss = 0.28 and accuracy of 0.92\n",
      "Iteration 2600: with minibatch training loss = 0.207 and accuracy of 0.95\n",
      "Iteration 2700: with minibatch training loss = 0.32 and accuracy of 0.91\n",
      "Iteration 2800: with minibatch training loss = 0.286 and accuracy of 0.92\n",
      "Iteration 2900: with minibatch training loss = 0.193 and accuracy of 0.92\n",
      "Iteration 3000: with minibatch training loss = 0.188 and accuracy of 0.94\n",
      "Epoch 4, Overall loss = 0.316 and accuracy of 0.894\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pc1qdFg0NjbZBQWsrMcbSAMYQUTcArxDRKAB/APAgDB70IIBfAfiS+1wiug3AbQBQVVWF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\nmroquWyYvq7hsz6ftYHp6yo+6wu1zZs37xVVbSnqJKpa1Rfw98DNuep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      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c99e9c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 3100: with minibatch training loss = 0.241 and accuracy of 0.91\n",
      "Iteration 3200: with minibatch training loss = 0.145 and accuracy of 0.95\n",
      "Iteration 3300: with minibatch training loss = 0.202 and accuracy of 0.91\n",
      "Iteration 3400: with minibatch training loss = 0.246 and accuracy of 0.89\n",
      "Iteration 3500: with minibatch training loss = 0.0711 and accuracy of 0.98\n",
      "Iteration 3600: with minibatch training loss = 0.19 and accuracy of 0.92\n",
      "Iteration 3700: with minibatch training loss = 0.112 and accuracy of 0.95\n",
      "Iteration 3800: with minibatch training loss = 0.0806 and accuracy of 1\n",
      "Epoch 5, Overall loss = 0.148 and accuracy of 0.953\n"
     ]
    },
    {
     "data": {
      "image/png": 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vdc8+/HABtpXGXMc6Wpca/TY3N0d6NjjvT5uOPI8e9f3pM1CUZ18/c+ZslBbI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\nxxvPDHUO8ZrIa2iRa/1nR/V3rU8INn7ur8vEHM/i/N9AsPL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c99e9080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 3900: with minibatch training loss = 0.0899 and accuracy of 0.97\n",
      "Iteration 4000: with minibatch training loss = 0.283 and accuracy of 0.94\n",
      "Iteration 4100: with minibatch training loss = 0.0842 and accuracy of 0.97\n",
      "Iteration 4200: with minibatch training loss = 0.0264 and accuracy of 1\n",
      "Iteration 4300: with minibatch training loss = 0.0438 and accuracy of 1\n",
      "Iteration 4400: with minibatch training loss = 0.0697 and accuracy of 0.98\n",
      "Iteration 4500: with minibatch training loss = 0.047 and accuracy of 1\n",
      "Epoch 6, Overall loss = 0.08 and accuracy of 0.975\n"
     ]
    },
    {
     "data": {
      "image/png": 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CME2dA8JyAFINoWr7vqNGK9D3+sz1yf28dn3tm7UY+8iU8AIGQMWtFFXyK2n/hc4cKgxd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\nMlLKoSBk08+xGk7lUBDyAWijN25UiPLZZDodwJeFJB805bA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c9e4eeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 4600: with minibatch training loss = 0.105 and accuracy of 0.97\n",
      "Iteration 4700: with minibatch training loss = 0.0421 and accuracy of 0.98\n",
      "Iteration 4800: with minibatch training loss = 0.116 and accuracy of 0.94\n",
      "Iteration 4900: with minibatch training loss = 0.0489 and accuracy of 0.98\n",
      "Iteration 5000: with minibatch training loss = 0.0377 and accuracy of 0.98\n",
      "Iteration 5100: with minibatch training loss = 0.04 and accuracy of 0.98\n",
      "Iteration 5200: with minibatch training loss = 0.0242 and accuracy of 1\n",
      "Iteration 5300: with minibatch training loss = 0.0292 and accuracy of 1\n",
      "Epoch 7, Overall loss = 0.0522 and accuracy of 0.984\n"
     ]
    },
    {
     "data": {
      "image/png": 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3o6725pOwEuOnWZSaIFAhiM9hrnhAREkAV+eHnPihm4FEDaELrTl4agC6OsR7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\nbU9fMEvc7nmq2UrRnLw+CYxcFh09lImDahg/0L8Xt3jOUE4cfRADbSX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c9c2cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 5400: with minibatch training loss = 0.0195 and accuracy of 1\n",
      "Iteration 5500: with minibatch training loss = 0.0574 and accuracy of 0.98\n",
      "Iteration 5600: with minibatch training loss = 0.0993 and accuracy of 0.95\n",
      "Iteration 5700: with minibatch training loss = 0.0289 and accuracy of 1\n",
      "Iteration 5800: with minibatch training loss = 0.034 and accuracy of 1\n",
      "Iteration 5900: with minibatch training loss = 0.0727 and accuracy of 0.94\n",
      "Iteration 6000: with minibatch training loss = 0.0624 and accuracy of 0.98\n",
      "Iteration 6100: with minibatch training loss = 0.0738 and accuracy of 0.98\n",
      "Epoch 8, Overall loss = 0.0764 and accuracy of 0.975\n"
     ]
    },
    {
     "data": {
      "image/png": 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5WD7rI+y8zgvahheZWWyblfs5e+2++Of1e2pw3RNL4+a2XBg5BDErnSWEGJlx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\n+hrvjZdyk3NCZOco4XsIxRWkWu8K80MW35TMASW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c9bb7f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 6200: with minibatch training loss = 0.0824 and accuracy of 0.97\n",
      "Iteration 6300: with minibatch training loss = 0.0339 and accuracy of 1\n",
      "Iteration 6400: with minibatch training loss = 0.0593 and accuracy of 0.97\n",
      "Iteration 6500: with minibatch training loss = 0.0236 and accuracy of 1\n",
      "Iteration 6600: with minibatch training loss = 0.0163 and accuracy of 1\n",
      "Iteration 6700: with minibatch training loss = 0.0107 and accuracy of 1\n",
      "Iteration 6800: with minibatch training loss = 0.172 and accuracy of 0.95\n",
      "Epoch 9, Overall loss = 0.0672 and accuracy of 0.977\n"
     ]
    },
    {
     "data": {
      "image/png": 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NtokTJy5h5qKIDsLMQf8ALHOyLcj+eQCWALhE/V5p+P1QqGMUFhZypJSUlES8b3MQqXzT\nP1nDve6cyc9/uzm2Ahmwkq/XnTO5150z+WhDk6t1OyGR72+wa5cIJPK1Yxb5okGTDcBidthWG/8c\nrQSnjhYAAKpD2VGqbyJKh5Lq+032O7AriKiL+nsXAHvt9hdCw2LcEQTBBZw08o8D+IGI/qt+/zmA\nh0PtpJqMXgKwjpn/rvvpEwDXAHhE/f9xWBILCUMihNMKguAOIUcOzPwfAJcAqFD/LlG3heJkAFMA\nTCKi5erfOVCUwhlEtBHA6ep3IULi2UC7NWpZWV6Jc5+chyMNTa4cXxCE0IQcORDR68w8BcBai222\nMPN3sI9zPC0sKQVb4tl5d0sxPfjZOqzZVYWV5Ycxrm97dyoRBCEoTsxKw/RfiCgVQKE74ghOKHxg\nDg7UNsRbDPF2CEISY2tWIqK71KVCR+gS7lVDcSCLnyCO6BVDPM1KXnE6CELSYqscmPmvrCwV+hj7\nE+61Yub2zHxXM8ooBCEcu//Hy3di097q2NXtsm4Q3SMI8SOkWYmZ71JDWQcAyNJtn+umYELsuW3G\ncgAxTPomjbcgJC1OHNI3ArgNQHcAywGMA/ADgEnuiiY4IRmjlTQk46sgxA8nk+BuA3ACgG3MPBHA\naACVrkoltAjE7CMIyYsT5VDHzHUAQESZzLwewCB3xRKag0l/K8XrP26LeH+3dYMoH0GIH06UQzkR\ntYWynsMcIvoYQOQtihBTOIoWdMv+Wtz70eq41B0MsSYJQvxx4pC+WP14PxGVAGgDYJarUgktAq9L\nPXsZMAhC/HGaQO94AKdAeW/nM3P8Z2AJACI3vcSi1y8OaUFIXkKalYjoL1AW5WkPoAOAV4joHrcF\nE9wlJr1+mecgCEmLk5HDlQBG6pzSj0AJaX3QTcEEZ0TafsZidrNbbbcMGAQh/jhxSO+CbvIbgEwA\nO90RRwgXp2280YzkicHQQXr2gpC82I4ciOgpKJ3DwwDWENEc9fsZABY2j3hCrDDqgtiMHNzRDqJz\nBCH+BDMrLVb/LwHwoW57qWvSCGHjtIE2jhxi4XOQkYMgJC+2yoGZX2tOQQR3MSqDWJiV3MrKKj4H\nQYg/wcxK7zLzZUS0ChYjfWYe4apkgiOcts/GhtwrPodmx61Jg4LgBsHMSrep/8+L5MBE9LK6715m\nHq5uux/ATQD2qcX+zMyfR3J8QSGe0UpCeMglF1oSwcxKu9X/kabKeBXA0wCM600/wcx/i/CYxzyR\n9j6NysATC4e02/Mcksw1nVxnIyQ7TibBXUJEG4nosG5FuKpQ+6nrPRyMiZSCD1OD7LCFNhbzemMg\nizR3YSGjNaEl4WQS3KMAzmfmdTGq8xYiuhpKNNQdzHzIqhARTQUwFQAKCgpQWloaUWU1NTUR79sc\nhCufsYHZum0bSkt3h9zvaJN/v9LSUhw46g34vrPai/c3NuA3ozKRluJ3CQeT78cfF2BrrpOpMuFR\nWXkUALBi+Qo07EgNWjaR769RtiZv4D2IN4l87QCRLxpiIZsT5VARQ8XwLIAHoIywHwDwOIDrrQoy\n8/MAngeAoqIiLi4ujqjC0tJSRLpvcxCufI0eLzD7C9/33r16obg4dAb1qrpG4KsvAQDFxcXYcfAI\n8G2J7/tFz8zH8r1H0a7fKBT2ahdcvlmfAQDGjB2LPh1yHcvulGc3/AAcOohRo0bhxH7tg5ZN5Ptr\nlK2+yQN8qeSsTASZE/naASJfNMRCNifKYTERvQMlZXe9tpGZPwi3Mmau0D4T0QsAZoZ7jGMd48jB\n8QxpgxnJbr9wkt25HX2TbGYrsSoJLQknyqE1gCMAztRtYwBhKwci6qI5ugFcDCDyxQSOUSLOwgpn\nDulwju9Wyu5kzcYqykFoSThZz+G6SA5MRG8DKAbQgYjKAdwHoJiIRkFRLmUAfhXJsY9lTCMHh71r\nNybBuRV/k6yNaLKNhITkJtgkuD8x86O6HEsBMPOtwQ7MzFdYbH4pfBEFADhU24BpH6zEX84fFrA9\n0klwRpNQJL31ZG3E3UKul9CSCDZy0JzQi4OUSXi8zCjbX4veLjhOm5NX5m/F7DUV6NEuJ6L9jQ1T\nTOY5RH2EeFfQvEgoq9CSCDYJ7lP1f4vOsfTeT434fHYp5v1pInrkR9awJgLpqUrIaH1ToGfZaXPT\nklJ2a6MYt3wa8SLJTkdIcpxMgisiog+JaCkRrdT+mkO4WLD+oAcAsK+mPkTJxCY9TVMOnpBlL3xm\nPp77dnPAtgjnzgXFbRt6stnoZeAgtCScRCu9CeCPAFYBiMG8WiESbEcOFg3Oih2VWLGjEr8+tZ9v\nmyl9htfogwhfJrcaO+24SdeYJtv5CEmNE+Wwj5k/cV0SISgZqYqtpb7RaFayb3G27KtB3455SjmH\nPodwHNNu29CTrS0Vn4PQknCS++A+InqRiK5Q8yxdQkSXuC5ZjGnpofP+kUNos5LGpMe/9X0OFa1k\nZG91HQ7VNgQt477PwZ0KLnvuBzxbujl0wTDYdqAWFVV1QcuIahBaEk5GDtcBGAwgHX6zUkST4OKB\nmy/kQ5+txaEjjfjbz0e6WIuCnVnJ6QmaRg4hDIRjHvoaAPDq5DhGebl08xaWHcTCsoO4ubhf6MIO\nOfWxUgBA2SPn2paR9RyEloQT5XACM4dO3pPgkAvTbl+YtxUAmkU5ZKgO6YaIo5UCv2s+h5QoLouk\n7A6PZIu+EpIbJ2al74loqOuSuEWSvJBaplTTyMEhdmalaJSm69FKSXLvNJJN2QnJjZORwzgAy4lo\nK5TEewSAW9oyoZE2gRVVdcjJSEWrrPSYyhMuWrNi9Dk4NVUYS8Uit5LbjXfS9bST7XyEpMaJcpjs\nuhQJzNiHv0ZB60ws+PPpcZVD6/kbzUrh7q+hmZXMStN5C+Z2WxeOjX7d7irsra7HqQM7uihRdLRk\nZcfM2Li3BgMLWgUtd+d7K5GVnoLpFw5vJskEtwhpVmLmbVZ/zSFcLIjF+1hRFTiBbv6m/TjS0BSD\nIztHa1jqjKGsETqkte+aVUn7OZwGLJFCWc/+5zxc8/JC12SJBS3ZrPTK/DKc+cRcLC4LvrjjO4t3\n4LUfWkzzIAQh9st4JRja6xgrf/SmvdW48sUFmP7J2tgc0CFaL7rOaFYKc38Nu/QZiWRWSjqfQws+\nn5XllQCAHYeOxFmSxGTT3mplIa4kIumVgwapBhSvl3H4aGPEx9lZWaf+PxoTuZyiNSweT/AWxmvX\n6Bu+az4HMhiWwhsNmMs+MecnzFodetlSR0dvya2pBU7PZlHZQfSe9hl2H27eZ8wJSXZLYsKOg0dw\n+t/n4uHPY7VgZmJwzCgHbUj/5DcbMXL6l9gfYa4lTbG0yXbmoP5mfQXKY9Db0hrtJkPj/9/FOyzL\n2e2v4Wt4KXg5jYYmL3pP+wzPlGzSHcNc7p9fb8Sv31hqeYxwufnNpVi3uyomx0oE7BS3kTd+VMwy\nC7YEN+EIsaOu0YO/fLxaWU43TA6qk0UXlx2KtVhx5ZhRDtp7+fkqpVcbrXJo7VA5XP/qYpz9z3kR\n1aVHk98YZVRV1xQQwWTX/jidBGfXM6ypV3wsL8zb4i9rL27MeH7ultCFdMRmEaPEoCX7KFoaby/c\njv/8sA1Pfb0x7H39frvkul/HkHJQbly0w+IqVTmUbtjreJ/quuid15r8Vo2f12suZ7e/ht+sFLyc\nRpNaSZpu1lxzmBjCdRXVNTpPL3L6378NmSIklji9Xto5J7sJ57dvLcXge7+ItxgA/O9VJG4DzTRr\nvF8HaurDeh4TDdeUAxG9TER7iWi1bls+Ec0hoo3q/3Zu1a/hz/AZmzdNG3buPhw8j06s4SDKoUmn\nHexO0xytpE2CC9xu1fE+2uBB2X7FNJZCeuXgTusV4AcJUzscDeNl3LS3BnPWVoRXQRQ49ee4MZs/\nVsRStM9W7jZF34XD/E370XvaZ9hxML5OcrtrUvjgV7jqxQXNK0wMcXPk8CrMcySmAfiamQcA+Fr9\n3iwY30ujI9YpTSEcwm4RrF3RKwy7BsgufYYRq/3Pe2oeLvv3DwCAVN3IwS0LTjTD83B7as2ZKTXJ\nBwLNjuZvW7zN7Js5UFOP/n/+HItChN7GEqtHafG2luuHcE05MPNcAMY7cyEAbWW51wBc5Fb9PjnU\n/8aGLNIeUDiNSSx71sEa4kadwjL6JLbsq4HHy0EmwRG+WLUbK3YooYpWMm/eV+v7rFcOzWFjDVeJ\nh+qJGs+vOV0U4T4PToo3ebx4Ys5PETlSWzravbN6RhaVHUKTl8P2WQl+nMyQjiUFzKzFOe4BUGBX\nkIimApgKAAUFBSgtLY2oQq/XA4CwdNkyHNmWitpaZQi6aOEi7Gplrxs9XsaHm/wvnFZ/ebnZkW0n\nm75BtitTU1Pj6NzWb7d/+b+bPx/tspRzqW0MbFEmPf4tLu6fjuM6pAbIsq5cOZ7X68HNb/qji+Yt\nXokj2/2PRU1NDfS2nYZ6vzlt+fIVaNjhPy7bnO8XWxtR28i4dGBGqNMEAFTqwoQrKvagtNS+92W8\nfvN/XIDy1qm25Y1Kcv2GDSg9GrsGRC+LUbbdNV7LckYqKpRnbP36dSit3mRbDgAW7G7CsyvqsWLD\nVlw7PDMsWZ0+ewCwp0K57+vWrUe7w8FlAoKfn9Oyevm8zKj3ANlp/mexwifTOrQ9HOhIXlOh+Pn2\n79/vSJbNZcr7UF6+A6WlzvyJmnzbqpTRarXN9Yy07YqGcO6tHc2tHHwwMxORbd+ImZ8H8DwAFBUV\ncXFxcUT1pMz/AoAXI0aOxEn9OiBn6bdAbQ3GjDkBAyxSATQ0eTHp8VL065iHb7fs823X6i+tWgNs\nKwvYx062hiYvMPuLoGVKS0ttf9Oz44cyYO0ay9+KxozzrY99qLYB+HpOwO/V6e0w6vj+wI/f+2TZ\nvXA7sHoVUlNTAY/fFPPK6ga8Ar+TNi8vD4B/5JCXm4O9R5Tvx40YgfED/OkqGj3W53vtrM8AAE9P\nPTPkeQLAv3/6ETh4AADQpXNnFBfbZ731XT+1jmEjRqOod75t+SadjAAwYOBAFI/r5UiuoKj168/b\neG837a0GvptrKmfkk4rlwK6dGDx4CIoLuwet9uDScmDFCrTrWIDi4lFhiez02QOAD/csA3bvwtAh\nQ1A8upt9QYvrEGlZvXzT3l+JGYt2YPPD5/hGr+/tWgrs2Y2hQ4egeFSgTPVr9gDLlqBDhw4oLi4K\nKcqmeVuA9evQvXsPFBc7yzOqybdm12Hg+++Qm5uL4uIJjs/PTcK5t3Y0t3KoIKIuzLybiLoAcB7y\nEyVGx7SdVtpbXYfyQ0dRfsh6AlI4poFY2rODmT9C+RxyM9NsfQ7hWtdSyd7nkAhhpKFyTxlFbM6J\nduFWFf+rmTi8t6QcgPKMacpBu54pFjbi5lxqNlL/ZaLT3KGsnwC4Rv18DYCP3a5Qe/mNtkfbkM8Y\nzoCPrXKwP5Y+WsmqfVaUg/UkuHAjYwIc0obK3EgfYCfeml2H0XvaZ9hwMNABHUo/Ga+j04lpGnWN\nnrBW49PjuCYtbt7B85Ps4a5G9H4u7V5aKQchetwMZX0bwA8ABhFRORHdAOARAGcQ0UYAp6vfm4Vv\nf9oX8N1OCTSF0A5WbclL320NmDkcrGy4eLyMRo83aCOgnzVt1aDkZqQGNEzMHHEvPzBaKfAYbkRy\n6XtlzIwxD32FdxfvwPebFLPT0r2Bc0i8zKhr9PgU1aryw5i3MfDe6wlX4sH3zsJJf/0mzL38sjkh\nEXuiiaKE9K+nMXkkoHQaDtY2+LY1h96wCswIt9ORiLgZrXQFM3dh5nRm7s7MLzHzAWY+jZkHMPPp\nzBy3/ABOJ4sZMaavAIAHZq7FY7M3mLbrG+DvN+3H3hBrDFtxybPfY8DdXwQfOXj0vSnz7zkZaQEP\nq5cBbZewzUo65WBUMFbXJlr0L3eTl7G3uh53vr/StryXGYPvnYWLnpkPADj/6e8w5aWFAb8Hlg9f\npgMRTpxzo4Ft7k5zvDrpVuuKa42yXqRzn/wOFzz9nb9MM7TRViYsu/VSWhLHzAxpDX9oq/XNC2UZ\nCadHoO/F//LFBb4GKxz8Iab2ZfSNstVDmZGWEtAIepkjtrUHm+cQatQVCfrGSFNGAaIbZNDu65pd\n1jmZWpIAvivjAAAgAElEQVTPIV7HTGT0z7fVyAEAyg8dbdaxly/7goXJKxqq6hqVAIo4kfTKwe4W\n2ZlVQjVw4fQIjHXsUmdVryyvxMX/mm87Yev1H8rQe9pnqK33m0yCPWwNTV7c/eEqbN1fa6m8vF4O\neHA9Xp1ZKcy3KDXIDGl3Jgham7HserCh9JN55NCMysGhEcu4xoajfcIXp0XCAalilP+xnlFe1+gJ\na1Etq6Yk2n5Sk8eLEfd/iXs/Xh26sEskvXIwod5Iqxta1+ixNA/pCWfkYKdI/vLxGizbXmnbu31e\nTW6nTw4YrNp7PlqFNxdsx61vL7PsSTZ6OWA7s31upVAEmJWMysFls5LXcA5WhGrs2fDSxlo3fLRs\np62Pw0095LaKi/b4y7YfiskozWMxbPTnorI6fvh1Dr53Fib/Y67j8lZ526I1K2nv0vtLdkZ1nGg4\n5pRDMLPSK/PLULrB3nkJOLvpy7YfwtLth0I2BqE6PAHO2CAP+U8VNQCAFLI+L4830KGtmJWUz1Vh\nJgW0a6wBuDIE1l8iJ070UEWM11ErP+Wl8Mx+4x7+2nL7799ZHuDjCKg73PYiSUxG8zbuw8X/+h6v\nfl8W8TF867FYmpWU3/SZAqIdTWzZXxu6kE8Oi9F6lMrBZ0KN40MQt0lwzYXdpbUaAQTLy/PZyt3o\nmZ/jqHd88b+UyWbfT5sUlky+3y0KOHnWiAgfLjP3NCqPNOKql/wJwDxRRCsFZIA1hbK6PHLQ1bdp\nb41l+VAvpfG0tfLzNu4PS649EQQXODYrhVlev08isuOgMmdow57qiI+hXYtVOw9jWJfW6NQ6SxfK\nqpRxw+flBO2Z0t+taKOVtHYmnkFPSa8c9MxYuB1lB5QegdVFD9bZ+O1bSoqJs4d3dlyfbUMVgUnH\nycOWQspiO0aMGWTZG3nPJj3N2gcAuD8JTj9q+++SHZZlQiuH+L1twS5PXaMHaSmEPVV1vucwnIFY\nPAcZr87fiqLe+RjerY3pty9W7cafP1wVs7que2URstNTse6Byb7rqc1z0HfcYpWi3wlW72a074LH\npxxk5OAeums77QP/QxrpRQ/npofqyNgNfa1E+3q9fzJ5u5x0HDpizrVkNxnIOGnLw+y4Z7OrJvAk\n2uf68/cYD9EYao6Il5GSEl4fV29a08ucnZ6K2gbzSC/cSXCRLvoUCcFs7oPvneX7PKBTHoDECocM\nJvv9nyrrqZc9cq7pt+ccJL5j5pBmIOU5UGTQ0rIbJQoI6Y5RR2X9nipM/sc8zP79BAzqbE63A+hG\nDsymbZFiGZnXzBxzPgcNK+XgZKZlOErFNn12BPsvV0NaAftV6OzkN2Yq9TLDw0oaglP6dwgqw0ML\nAtOI6Bss4wuoV5xWSjSStbvtfBxZ6dbJ9UI5PY0/vzK/LGyZIkVf9bYD9jZtzd4dTgPXXGalaGz5\n9kEEkR4vsHet93lpo4hwxX15/taA75+vVPKEfhFkXXSrlDyx8jnEk6RXDnaXeNFW8/w7J89RODfN\nruenbbY61j0frcJONSup3f5261en2NxN48jB62V4WQlLDdWTN3bOPQET7ow+B6/lZ43RD8wxbQtF\ngENaV19mmnKyxisU6v5Y+3Ni/yJ+vHwn1h1QLt73m/fjg6XlAXX/6vUltvtq55wIDUQo9NfO8rPF\npDUjxufou4378b6aS8kJ2mVqDNOstO1ALXYfts6hpqHtHmzWutVtivbexct/oifplYOF5QEA8OQ3\n5nQXTnoZwRzSvad9FvA9VKNjfIAaPV688eN233e7nmPrrPBGDvWmkYNybCIgNVTElOH3WWv2+D6b\nQll1imP6p2ssHfzRDPf1+2ZlWI8cQpmJrHp0kYrkH/qbD3DbjOX4v0WKr+eXLyzAH95dEVAuWOPh\nd0bGXznUNXpwVPcSGc+10WZ2fjjX1HieV720AHf8d0XI/bTdfEvoepxdX41THyvFiSFSodhNtNPj\nk19XZax8DvEkqZXDhj3VOFAX24scMo4+oAGwKQPzMFj5bjDT2NSVl2ntKrL3OViYldTslql2ww0V\nO+WqHCfwe6XObPT2wh3497dme3NDmOGuejOG/oXJSrNeR+Lhz9f7Ph+19EmYr2mkvTRtP6dtuL5Y\nqgPfSyI0ECc/8g2G/GWWT3bjueqvnaVZMWDSpHUdji6/xeXyjw6U/402skSDVYoOcxmFIw0ebD9w\nRJUpunrdmDMULkmtHNbvsZ5kZocTe2qoh05/U2vqzXMIek/7DAdqGkxlle+Bb4ldXTmZ1r3m7zZZ\nh2Mae/AenVkpNYonwNiL3F0ZOER/4qufTA20UVE5refmN5YEJE/U12x3nYb8xe/k5SBmBqe6wW5G\nuNPXWL97WqghG5w1ELFoQrbssw4LbvR4TXmkjKahRhszo5UStpM10hGSf+Sg/G+ykCXa6xPOyGFP\nVR0mPFaiJLY0nFNdowefrtjluN5ESNyX1MohI5qWz4ZQDYl+2v3Pnv3esowWWmps1IzHtqsrP8fZ\nimoaxgaZWXmgiZz1YO04VBvoYN5VabbfriivDPjuJK23/kUkUkYbX6zeg/s+8S92pH95nET1BAsN\ndBoVZFLmnvDMP/pyqU6CH5qhgfhwWTkmPf6t5azuP1qYdozPpH70G1I52I0cIlAOTR6vT1Fp+2vP\nFlF4YcDB8PkcgtwvU6fBGziP6PDRRjz42Vrc8vYyLNhywFG9MnJwmfQwlMPuw0fx+g/bQpYL1ZCE\nk5OlvsmLQ3W6obBx3oDhe0ZqClbefyZydPb2bm2zHdRjDmV1alYKxhNf/RQwOrKaGLbRMFnNyfUx\nXuLaerN5SD/KcpLTyRNk5OBxOHnPnIVWkcFp46YvlhJkXQxffQ6OG0zFeLyhQ5a1yX+7K8337qPl\n/p6upvhNadoN2X71dTslknaw/91fYN3u6oD9NVlSiGIWBuzkMEaF2dDkDVAYf/5wFXap19fKmmBF\nIpgUk1o5ZKQ5P73rXlnkaNZrqJsWjk39sdnrcXvpUVSo9YYyKzV4vGidle7rxQwsyMPYvvZLYmpY\n+Ry8aiirA+tGUKrVhe1X7zyM9RYzYBsNdWvKoaquEac9XopV5YeDHp9AAQkINfSzUp0onGhGDvd9\nvBq9p31mYQZkVNU14oiF8rJCb5JJJQIzY2NFtW0v0cnIIViJAXd/jgue+c7yt4O1DWBm7FdNnO3z\ngo9Gl223zg6sHwkGrEhocUvsopX++vm6oHXboYVGaw2xR600lch37aKNRNNkDhbmbnymGpq8Ac/U\nYYs5SaGQkYPLhJq3oH+YDzrM0R9SOYQxcti8T4ln1xpYk1nJ5sH2Ze1kZwvDGA+jhLIqE4/CnZRm\nRLse5z31Hbapzjg9RjNSXZMHzIwfNx/A5n21+OfXPwU9PhFQ22BWDvr74EQhB0tHoD/Wut1VPlPJ\nXz9fh6IHv8Jr6ojSFEDgZYy4/0uM+6t1niUjxpHDv+duwRlPzMWy7Ycsy4dSWg/MXItHZ6kOeEuH\nLbB6p9nvVtfowfEPzMGLqxp8IwKn5kVjA68ftekbYss5LTanM2OR9Wx3PUEdwupxNf9HSkoMe96O\nfA6B3xs8XlP9/pUX/dvu/Wi1KcJRwyOhrO4S6uVqtLGXBj1mDEcOGv7p/84c0lr53My0iBZf8bK6\nFi9R1B67P7y7wqfcAGXmsh6tx6kx+R/z8Mf3VvpmuVpNZguc+MaWZiX9GtjGUF0rNNORVU9Sf53P\n/uc89L/7C5Ttr8W/524JCI01+xyUep062fUTGVOJ8KKafbfaJvlhqEfppe+2Ym+1Kl+Q+3jtKwvx\nv+osZsAfxTV/V5MvR5XTdOvGR9IuWmnCYyWOjhcL/JPgdGalmEUrOShjMXLQv8pLtx+ynC/x+o9m\nM/b2A0fUlRojEDbGxEU5EFEZEa0iouVEtNitekJpX7shcTBCKZFwRg6+fVQ5zA5pm5GD+r9Hfo5t\nj6pvh1zTNm02tGJWUnqL0cbSL9x6EP/R+WrycwPNE/p5ERrvLSn3NVA5NvMVNJiBIxYjh73ViimO\nATR4Qpt1tI6C5cjB4hpYmciiXflOnw4+Iy3FN3PdTrlY3ZujDR7sORxe0r/SDfsCZv5apTlxei7m\niY9ag+zcd/D6D2VBZ4hrLNjtzD7vnwTnVWUJ9DnUNXpQsmGv1a4+rF6DtbuqfGvPB+uDWY0c9Nfp\nSIMHS7ZZjw71LNx6EBMeK8F/l5Qf85PgJjLzKGYucquCUL0h7fe6Ro/jB9uq0dDzjoMhspHGJrXn\nYxw5hGi4e+Zn244cbjmtv2nbKQMU5aA5KmMwcAAQaL5rl+ufoGcnW1oK4YiqHIwjDSDwRVVGDuZG\nQh9C6aTn7vEyauqbLJW3lRK2iqoybotmcSNNHsCfK8hIk8e8Yt81ryy0NmMZrrUxCEGPVfZc550j\n47H8DbJlRyNghjRQW9+Eez9egytfXGAua+DZFfWoPBLa3GucBKdEKymfSzbswxUv/IjrXlmElYbI\nOatj6LnuVX/q9Wh8DoB/dPjY7A22JuwNauj9ih2V4pB2m1C94kavF00eL0548KuI8v5YEUnOeq3n\n6zTL6c8Ku+OsYQU497iutg+tVaSWFtrLDF9upVg8g2k6e3U7XZhtjk3+oyYv493FihLNzgie+9HO\nrKTHiXJo8jKG3zcbU142N0pW19lqprWxUY2kdzeyR1uM6tE2YE7KUYuREaDk+SlSn03t+Vyopn35\nnxAziGuCrNNhte6G03OxmyGd4mAUysw+/9GRYLMrdWiNajDzqSaTdg6pKRSg8DXT5t4q+9nzod4D\nq/pnlzWi97TPzIEjTV7bUf/a3VW2q7tpo7f01JRj2iHNAL4ioiVENNWtSkJd4EYP42ijB9UOw8vc\nYq0akmeU1+5l69o2G/+eUoShXVvbvjRWSiNdjd6qqKpDfaPXFzETLXqndludcggWLaaNwPZV1+Oz\nlYFJzYzJ9o4EWWcDcGbK0xrESovIkYMWvdN91eaGpNRgmohk5HDjKX1M82/sRg4AcKC2ASOnf4mi\nBwPzUr0XIvdQsJBJq1GR056qaYa0b+TgIK8V/GHJViNGK/wRSfZlfGYl9X6k2oSyatd5ybZDpsY7\nEvPqjPXKc2N8/i58Zj5ufXuZ7X5zN+zDk7rU+j7lpsqflkKOw6vdJF4pu09h5p1E1AnAHCJaz8wB\n6/KpSmMqABQUFKC0tDTsSlbtCt7oz//+B+SkReDRjTH3frQaPeq2YltVYCOxYqU5D77xOuzeZd0b\nWrV6jWnb1k1KZNCN/1HcPF1yCRV7w1+0xsiWzf48VdUH/A0oe0Ir3feXluP9peWoLc9Gpxyl0Tx0\nyD+ZrnznLvBhs99Co7GxEYuW2L+IGnPm/Wj72yX/Mk9WXLXR7CycrnPqAsCixfbJ8zRKSgIdsxvX\nr0VtdaCC0qf8sKPRw7j66dm2v1fsqQh4NozPEuB/dnZUm5XD6rXrkF9lzjdm5KefNqK0ocz3fa2a\nXJC9XvzwQ+A1/qakBFXV/ueroqIC385XRj6exsDn7vVPv0GPVubOxNwfF2N/+1R4g4xs1q/fgNIj\nW7Byp3JdPU2N2LK1zFRu+ao12LFpHR5dVIfLBvnNn6Wlpdi8xdxBqK/3b9u8eTPerS7D0goPJvdR\n9tWa79XrzPdvVxC/UHV9E/4+xx+lV1Jaiu1VXpRsU96XXTvLsaLWP8ckkravpqYmov30xEU5MPNO\n9f9eIvoQwBgAcw1lngfwPAAUFRVxcXFx2PUcWFIOrLQffh9fNAats9KAb5yFIrpJcXGxYhP93r9U\npbdtDwCbTOX0fHloFVC+HUYGDxkCrFgesO24YUOA1f7r0SovFx075gF77BtfJ8zSVT+wb0+UlitO\nvNzsLFTWB896qZHRZRCKR3cDADy/8UfgoDKTtHPnzujRIRdYb722d3p6OgYPOw5YEjyu4cEfw1OC\nrfM7Ajvt0zQDwHEjRwEL7JUOAKzh7gD8DUHR6JFYdWQr1hwIvhytFXPL7ZVtp4IClGe1w6WF3QEA\nP/1QBiCw0dKenUKL7Lj9BwxC8die2HagFkcaPBjSpTUwyxxmufRwFh4qHu/fsGEvsGgR0tPScMKY\nscDcUt9Pp4w/Fa3X/gAcVsw6nToVYNDwnsAPPyK/dSvsqfWH2d47/6h/PQhdvb0GDEXxiC5I/foL\n2/VCBgwciOJxvbB74XZg1SrkZmehR8+uwObNAeV69RuAnIw0YNEKNOZ0ArDTd11WejYCGwPDqivr\n/b33Xn364m/fbcXe6gb8+YpiJfmlKmeffv2BtYEdh3A4efwEXH+PP9VLn949MaRrG2CZssDY+Amn\nhp3JoLS01NRWhEuzKwciygWQwszV6uczAfyvG3WFGuYeqKl3PL/BbRqazLHRT1lkjjVi98x4vIzF\n95yOR2etx7uLFROE0Q+RQuQ4r1Aw9Pl3cnU+hMwwJiHapdXwMrB1X/DIlkgixELhxAZ/+fPBFQNg\nNv9kpqWENTnTKZ+v2o0Pl+3Ersqj2FtdH9TsZMyXBPgj+059rBSA9cI9gDIPhFlZg3zrgVqfKYQs\n1i83xfrDb+7KSnd2DQ45cEiX7a/FVS8uwEn92wNQfA5Wj9PRBg9aZaVZyhbKrPTIF35Faywa7fNn\nlCU1JdDnUNvQZJuJ2U3iMXIoAPChOss3DcBbzDwr+C6REcrn4OTlbi6WbT8UUZ6jcX3bB6T51jil\nfwd0yMsMWPvBaOuubWiKeVroXF1SQCfJ5TT0L4hepKXbDgVd7J05eGROpMxeUxGT4+RlBb5imemp\ntgsVBaNVZlpQ35jmlK+ua8LiMvNaJRp2jtJwHKANHi9enV+Gv36xHrdOUqLirMKiF287iBW6uR2s\nizyzUpBWK8IFW9dd48XvlDDdKnW+TZqNc/xIg8d2TlE4/l+jQ99JvrCgxzNUnp5CAWH4NXXxUQ7N\n7pBm5i3MPFL9G8bMD7lVVyLMMnTK5c//GFH42nkjumLxPacHbPv96QPQqXUWACBTl9o63fBC7jh4\nNOYLmOvTiWvnM7AgL+R++oVa9LNwgykGpaw7I4dYYZyhnJmW4tgZq6dNjrPGITMtJWj0ll1UXjjP\nXl2D19cgb1VnxSuhrIHlpry0MOA7Az4FZxVNd9uM5TjziW8DtjV5Gf8q3WRazdBSLlWRbNlfiw+W\n7jT9XlFV51MODU2GUU0YnaRGD+OQbvQV7fNnlV5DH+xgFcrdHCR1KGsihIOFQ6SxzR3yMgO+6zN+\n6kcjmZaJCO3rjMT8oe8pa+ej2cGD4fF4sW53FS751/yQoat6vBxZGvB4kZmWEtHIwWlk1NFGjy/r\nrxGPly1NSoBFmG6Q3vCyHYd80VwN6qgtNYUc9aDr1QbcKsvpJyt24aeKwESNTR4vHp1l7W8yoo/6\nsgpF/qmi2qccvloXODIMZwRdXdcY4FBuiDKyyJi9udHjxR/fW+mvL07KIV7RSs1CLCeSZKWnOOq9\nOKVb22xMGtwpYAp9sJDGcEjVmXP0Dadx5AD4h9MvXVOEUwZ0wPtLduLPHypRUu1y0lERJDbcirbZ\nSihrCvkbHLtlTfW8MG8rHp/zkymVRKiZt16O7Lr9e0ohmIFfvxE64iiWZKanIjvErHArnM5DeHOB\n2cSo8f3m/baK6UhDU0APtSrIPIkdumiyI7qZ7poCy05Ptb0nWu9+7k/OHPJWE/bsCJUAsezAEdvQ\n73CaijOeCIidwXPfbrYp6Yy9hrBpo7IJNmfFTWTk4JAzh3aO2bEAYMLAjvjT5EEB2254LTaZRPST\n0vQ2W20UkatrnLQeUwoRMtNS0bmNfxQSiZ2zc5tMPHvl8XjjxrG+Bi3XZuU6PTsrj1rmGArlhymr\n8mLP4bqAc3ZCq8w0tHVoqoklmWkpAavYOSUWprMpLy20NVE89c0mDLvPHypbESRDcZ1uApvmYM5O\nT/Xd79OHFljux8xhj/LCmWhoNyrSONLQFCTwITZtRZR5LAEE+jAevvg4DHBglnWDpFYOsRw5hOr9\n/vmcwQHff3Z8cFOKx+uNadTKp787xfdZPwEuQDmo2/W9R98lUnfJ0UUbGXtZvxzbM6QcmWmpOPu4\nLjipXwdfxEio/El25GSkhuw5bqvy4tXvy8LuCHgZYSuUWJCZloLsjPDve6w6Ok4cvIBignFyDE3Z\npKWSz46fZfNcN3nYcf0az5RE1yvXU9fotXyejjZ4ol7WU6Oodz6KerULa59+HQPzoDV6vOjWNhs/\nO747fjm2J7q0Cb1mixsktXKIJveNkVC9zKkT+gV8D6VM8jLTka4utDOqR1tHMkyd0Nf2t+O6t/E1\n5vpGT5+mIMVCObBu5AAEhqIayXXQyGfqQhS7tFGc4uEsuqTHaYqFSGj0eqNOVx4JuRlpEfkcYuVX\ncWqCu23GctvfanX3RZtx7vH6e/l25/fN+r14Yd5Wp6K6gtXIachfZuFLiwSRkZBKFFaUHmCezd7o\nYTR6vMiI8wTdpFYOdtFKk4eZTURaSJ4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c9eb35f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 6900: with minibatch training loss = 0.0227 and accuracy of 1\n",
      "Iteration 7000: with minibatch training loss = 0.0406 and accuracy of 1\n",
      "Iteration 7100: with minibatch training loss = 0.045 and accuracy of 0.98\n",
      "Iteration 7200: with minibatch training loss = 0.0182 and accuracy of 1\n",
      "Iteration 7300: with minibatch training loss = 0.0591 and accuracy of 0.95\n",
      "Iteration 7400: with minibatch training loss = 0.0213 and accuracy of 0.98\n",
      "Iteration 7500: with minibatch training loss = 0.0175 and accuracy of 0.98\n",
      "Iteration 7600: with minibatch training loss = 0.0167 and accuracy of 1\n",
      "Epoch 10, Overall loss = 0.0462 and accuracy of 0.985\n"
     ]
    },
    {
     "data": {
      "image/png": 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F+6qiasdKynAfDA9il94bjjUQjhJuURtmBq575XsMeuBL72f6Tvq/qmui2bRn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\nwP8Dz0R6Ff717TcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148ca2216a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 1.5 and accuracy of 0.711\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.4999494400024413, 0.71099999999999997)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feel free to play with this cell\n",
    "# This default code creates a session\n",
    "# and trains your model for 10 epochs\n",
    "# then prints the validation set accuracy\n",
    "sess = tf.Session()\n",
    "\n",
    "sess.run(tf.global_variables_initializer())\n",
    "print('Training')\n",
    "run_model(sess,y_out,mean_loss,X_train,y_train,10,64,100,train_step,True)\n",
    "print('Validation')\n",
    "run_model(sess,y_out,mean_loss,X_val,y_val,1,64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Epoch 1, Overall loss = 0.052 and accuracy of 0.983\n",
      "Validation\n",
      "Epoch 1, Overall loss = 1.5 and accuracy of 0.711\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.4999494380950928, 0.71099999999999997)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test your model here, and make sure \n",
    "# the output of this cell is the accuracy\n",
    "# of your best model on the training and val sets\n",
    "# We're looking for >= 70% accuracy on Validation\n",
    "print('Training')\n",
    "run_model(sess,y_out,mean_loss,X_train,y_train,1,64)\n",
    "print('Validation')\n",
    "run_model(sess,y_out,mean_loss,X_val,y_val,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe what you did here\n",
    "In this cell you should also write an explanation of what you did, any additional features that you implemented, and any visualizations or graphs that you make in the process of training and evaluating your network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Added an extra conv layer, changed all conv layers to 3x3 kernels. Added batch norm for the first fully connected layer. Change to Adam optimizer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Set - Do this only once\n",
    "Now that we've gotten a result that we're happy with, we test our final model on the test set. This would be the score we would achieve on a competition. Think about how this compares to your validation set accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test\n",
      "Epoch 1, Overall loss = 1.66 and accuracy of 0.697\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.6564263843536378, 0.6966)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Test')\n",
    "run_model(sess,y_out,mean_loss,X_test,y_test,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Going further with TensorFlow\n",
    "\n",
    "The next assignment will make heavy use of TensorFlow. You might also find it useful for your projects. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit Description\n",
    "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
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 },
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